Multiparameter Identification Technique for PMSM Based on Harmonic Coupling Model Considering Torque Ripple Suppression

Accurate online parameter identification is essential to improve the performance of permanent magnet synchronous motor (PMSM). In this article, a novel multiparameter identification system based on harmonic coupling model is constructed. The multiparameter identification can be realized by injecting 11th and 13th current harmonics. By constructing two extended Kalman filters (EKFs) for fundamental and harmonic model, the system does not have the under-rank problem when identifying PMSM parameters. The system also takes the effects of controller time delay, inverter nonlinearity, and PMSM parameter harmonics into consideration. By constructing 11th and 13th current harmonic coefficient matrix, the influence of harmonic amplitude and matrix condition number on parameter identification is analyzed. Meanwhile, the influence of current harmonics on PMSM torque ripple is considered. By constructing a multiobjective optimization for current harmonics, the parameter identification accuracy and torque ripple suppression are simultaneously improved. The optimized current harmonics have a better effect in identification accuracy compared with harmonics with bigger condition number and have a good effect on torque ripple suppression through simulation and experiment verification.